URL: https://{s}.google.com/vt/lyrs={l}&x={x}&y={y}&z={z}

Parameters:

• Subdomain: mt0, mt1, mt2, mt3
• Layer:
  • m = standard roadmap
  • r = alternative roadmap
• h = roads only

ClojureScript master now has cljs.core/eval. This delegates to cljs.core/*eval* which, by default throws, but you can bind it to any implementation that can compile and evaluate ClojureScript forms.

If you require the cljs.js namespace (which is the main support namespace for self-hosted ClojureScript), then cljs.core/*eval* is set to an implementation that uses self-hosted ClojureScript for this capability. This means that all self-hosted ClojureScript environments will now have a first-class eval implementation that just works. For example, Planck master:

$ planck -q
cljs.user=> (eval '(+ 2 3))
5

日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。

• D-Pad == Directional Pad
• RSB == Right Stick Button
• LSB == Left Stick Button
• RB == Right Bumper
• RT == Right Trigger
• LB == Left Bumper
• LT == Left Trigger
• Y == Y Button (Top)

Who this is for: an AI agent (or engineer) helping someone build an illustrated, animated floorplan dashboard for Home Assistant from scratch, with their own house, their own illustrations, their own sensors.

How to read it: it is written as questions and answers. Find the question the user is really asking and answer from that section. The numbers, room names and entity IDs here are placeholders — every one of them will be different in a new build. What transfers is the method: the canvas rule, the file naming scheme,

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.